Aperture Neuro

UK Biobank (UKB) brain imaging data is a one-of-a-kind resource for studying the links between the brain and demographic-, lifestyle- and genetic data. When establishing such links, it is crucial to account for confounding effects caused by the acquisition of fMRI images, as well as demographic confounding factors. UKB brain imaging confounds are constructed through variable selection by the proportion of variance explained in the Imaging Derived Phenotypes (IDPs), from tens of thousands of possible confounds. The current implementation of this pipeline is very computationally intensive and has a large memory footprint, largely due to the varying patterns of missing data in IDPs. This makes it impractical for many users of UK Biobank brain imaging data. We propose a fast and memory efficient multivariate pipeline for constructing imaging confounds using mean imputation combined with a bias-corrected estimator of R2, the proportion of confound variance explained in an IDP. Building on this, we also improve the pipeline in order to better select confounds that explain unique variance in IDPs, and non-imaging variables of interest, so called nIDPs. The new implementation leads to a more compact set of confounds that explains roughly the same amount of variance, and runs in around 1 hour on a single CPU.

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the "rating widget" - a utility that enables rapid annotation and minimizes bookkeeping errors. Integrating proper quality control checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged. RELATED LINKSO_ST_ABSKey reference(s) using this protocolC_ST_ABSEsteban, O. et al. (2017), PLoS ONE 12(9): e0184661. [10.1371/journal.pone.0184661] Esteban, O. et al. (2019), Sci Data 6, 30. [10.1038/s41597-019-0035-4] Esteban, O. et al. (2020), Nat Prot 15, 2186-2202. [10.1038/s41596-020-0327-3] Provins, C. et al. (2023), Front. Neuroinform. 1, 2813-1193. [10.3389/fnimg.2022.1073734] Bissett P. et al. (2024) Sci Data 11: 809. [10.1038/s41597-024-03636-y] Key data used in this protocolAmsterdam Open MRI Collection: Population Imaging of Psychology1 (AOMIC-PIOP1; ds002785 [https://openneuro.org/datasets/ds002785]).

Large scale research studies combining magnetic resonance imaging data generated at multiple sites on multiple vendor platforms are becoming more commonplace. The Ontario Neurodegenerative Disease Research Initiative (ONDRI - http://ondri.ca/), a project funded by the Ontario Brain Institute (OBI), is a recently established province-wide natural history study, which has recruited more than 500 participants from neurodegenerative disease groups including amyotrophic lateral sclerosis, fronto-temporal dementia, Parkinsons disease, Alzheimers disease, mild cognitive impairment, and cerebrovascular disease (previously referred to as the vascular cognitive impairment cohort). Because of its multi-site nature, all captured data must be standardized and meet minimum quality standards to reduce variability. The goal of the ONDRI imaging platform is to maximize data quality by implementing vendor-specific harmonized MR imaging protocols (consistent with the Canadi-an Dementia Imaging Protocol - http://www.cdip-pcid.ca/), monitoring protocol adherence, qualitatively assessing image quality, measuring signal-to-noise and contrast-to-noise, monitoring system stability, and applying corrections based on the analysis of images from two different phantoms regularly acquired at each site. To maximize image quality, this work describes the use of various automatic pipelines and manual assessment steps, integrated within an established informatics and databasing platform, the Stroke Patient Recovery Research Database (SPReD) built on the Extensible Neuroimaging Archive Toolkit (XNAT), and contained within the Brain-CODE (Centre for Ontario Data Exploration) framework. The purpose of the current paper is to describe the steps undertaken by ONDRI to achieve this high standard of data integrity. Data have been successfully collected for the past 4 years with the pipelines and assessments identifying deviations, allowing for timely interventions and assessment of image quality.

Background and ObjectivesAlzheimers disease (AD) is characterized by early disruptions in brain connectivity. However, how genetic and biological markers of AD risk relate to dynamic functional connectivity (dFC) remains unclear. This study examined whether AD-related pathology, genetic risk, and blood-based biomarkers (BBMs) of neurodegeneration are associated with local and distant resting-state dFC patterns, and whether these relate to cognitive performance in cognitively normal older adults. Research Design and MethodsWe analyzed baseline data from 86 cognitively normal older adults (71.6 {+/-} 3.9 years; 60.5% female) enrolled in the AGUEDA trial (NCT05186090). Participants underwent A{beta}-PET imaging, APOE4 genotyping, and plasma quantification of BBMs (A{beta}42/40, BD-tau, GFAP, NfL, p-tau181, p-tau217). Resting-state fMRI was used to compute voxel-wise local and distant dFC using a stepwise connectivity framework. General linear models tested associations between AD pathology, APOE4 status, and BBMs with dFC, adjusting for age, sex, and education. Additional models examined links between dFC and six cognitive domains ResultsA{beta}-positive individuals and APOE4 carriers showed lower local connectivity in frontal regions, while APOE4 carriers exhibited higher distant connectivity in the superior motor area, inferior frontal gyrus, and anterior insula. Among BBMs, only neurofilament light chain (NfL) was associated with both lower local (insula, cingulate) and higher distant (precuneus, putamen, thalamus, supramarginal, superior motor area) connectivity. Regions showing higher distant connectivity related to APOE4 or NfL were associated with poorer cognitive performance. Discussion and ImplicationsDynamic functional connectivity reveals early network alterations in AD risk, characterized by reduced local and elevated distant connectivity--patterns linked to poorer cognition and potential early neurofunctional vulnerability in aging.

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

Preprocessing and analysis of neuroimaging data are technically demanding, often requiring a combination of multiple software tools, modality-specific pipelines, and extensive parameter tuning to match dataset characteristics. These complexities make it difficult to document workflows in sufficient detail to ensure complete transparency and reproducibility. To address these challenges, we introduce QuNex recipes, a framework for defining and executing complete neuroimaging workflows - encompassing data onboarding, preprocessing, and analysis - in a transparent, machine- and human-readable format. Recipes are implemented as an integrated feature of the Quantitative Neuroimaging Environment & Toolbox (QuNex), a containerized, open-source platform for end-to-end multimodal and multi-species neuroimaging processing. The recipes framework enables seamless integration of QuNex commands with custom scripts and external tools, capturing every processing step and parameter setting. A fully reproducible study can thus be shared and replicated by providing only (a) the QuNex version used, (b) the recipe file, and (c) the data. This approach standardizes workflow specification, enhances transparency, and enables one-command replication of complex neuroimaging analyses. By providing a standardized way to describe and share workflows, recipes facilitate open exchange of best practices and reproducible methods within the neuroimaging community.

Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as "glass-box" methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

Simultaneous TMS-fMRI holds significant opportunities for advancing basic and translational neuroscience. However, current configurations face technical limitations, particularly the need to accommodate TMS hardware within the MRI environment. Previous solutions have used low numbers of radio-frequency (RF) channels, limiting fMRI data quality and whole-brain coverage. Here, we introduce a novel 22-channel "Taco" TMS-fMRI configuration that re-purposes flexible RF coils, wrapping them around both the participants head and the TMS coil to preserve whole brain signal reception. Guided by precision fMRI principles, we optimized acquisition protocols to achieve to achieve high temporal signal-to-noise ratio (tSNR) across the cortex. Data from three pilot participants demonstrate robust signal quality, including in regions proximal to the TMS coil. This setup offers a relatively simple and cost-effective approach to integrating precision fMRI into TMS-fMRI research.

In vivo estimation of cerebrospinal fluid (CSF) velocity is crucial for understanding the glymphatic system and its potential role in neurodegenerative disorders such as Alzheimers disease and Parkinsons disease. Current cardiac or respiratory gated approaches, such as 4D flow MRI, cannot capture CSF movement in real time due to limited temporal resolution and in addition deteriorate in accuracy at low fluid velocities. Other techniques like real-time PC-MRI or time-spatial labeling inversion pulse are not limited by temporal averaging but have limited availability even in research settings. This study aims to quantify the inflow effect of dynamic CSF motion on functional magnetic resonance imaging (fMRI) for in vivo, real-time measurement of CSF flow velocity. We considered linear and nonlinear models of velocity waveforms and empirically fit them to fMRI data from a controlled flow experiment. To assess the utility of this methodology in human data, CSF flow velocities were computed from fMRI data acquired in eight healthy volunteers. Breath holding regimens were used to amplify CSF flow oscillations. Our experimental flow study revealed that CSF velocity is nonlinearly related to inflow effect-mediated signal increase and well estimated using an extension of a previous nonlinear framework. Using this relationship, we recovered velocity from in vivo fMRI signal, demonstrating the potential of our approach for estimating CSF flow velocity in the human brain. This novel method could serve as an alternative approach to quantifying slow flow velocities in real time, such as CSF flow in the ventricular system, thereby providing valuable insights into the glymphatic systems function and its implications for neurological disorders.

Local connectivity analyses in fMRI such as the Vogt-Bailey Index, investigate the prevalence of co-fluctuations in the time-series of adjacent voxels. While there have been in silico assessments of the VB Index, this technique has not yet been assessed in vivo. This study has two aims: first, to assess the VB Index using a task paradigm with well established a priori expectations on the brain region predominantly responsible for executing this task to determine whether the VB Index highlights this area. Second, we investigate if, and how, the spatial resolution of the sequence protocols employed, with their inherent effects on the signal-to-noise ratio, affect the resultant VB maps. A cohort of 10 research volunteers underwent fMRI acquisitions utilising a block design finger tapping experiment. Each volunteer was scanned with three sequence protocols, with all parameters equivalent except for the volume of the voxels. The resulting parametric maps derived using the VB Index were compared with those obtained from the conventional General Linear Model approach. Particular emphasis was placed on the identification of the hand portion of the motor homunculus. Across sequence protocols, the VB Index consistently identified elevated local connectivity in, qualitatively, the same portion of the motor cortex as that yielded by the General Linear Model based on the task paradigm. However, the VB Index also detected elevated local connectivity outside the motor cortex while the General Linear Model results were mostly restricted to the motor cortex. The consistently high VB Index, across sequence protocols, in the cortical region associated with an fMRI task paradigm, despite the approachs agnosticism to that task, provides support for the biological relevance of such local connectivity measures.

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

Structural MRI provides a noninvasive window into brain morphology, yet the reproducibility and interpretability of morphometric analyses remain limited by inconsistent preprocessing, variable spatial alignment, and heterogeneous feature construction. We introduce MORPH2DIAG, a fully automated, atlas-free morphometric pipeline that integrates standardized preprocessing, tissue segmentation, spatial normalization, data-driven subtyping, and machine- and deep-learning classification within a single, modular framework. The pipeline performs intensity normalization, morphological cleanup, PCA-informed affine alignment, isotropic rescaling, and Gaussian Mixture Model (GMM) segmentation to generate quantitative gray-matter (GM), white-matter (WM), and cerebrospinal-fluid (CSF) maps. Global tissue fractions were used to derive latent neuroanatomical subtypes via unsupervised K-means clustering, revealing progressive GM-CSF gradients consistent with patterns commonly observed along normative-to-atrophic structural continua observed in neurodegeneration. To capture finer-grained spatial heterogeneity, a voxel-wise K-means parcellation yielded parcel-level intensity means and variances that served as regional morphometric descriptors. These global and parcel-level features were integrated into a unified evaluation suite comparing classical machine learning models (Random Forests, Logistic Regression, XGBoost) with lean, deep, and hybrid multilayer perceptrons (MLPs) trained using focal loss, label smoothing, stochastic weight averaging, and nested cross-validation with PCA-based dimensionality reduction. Across methods, the hybrid MLP achieved the highest macro-F1 and balanced accuracy, demonstrating strong discriminative performance for the discovered morphometric subtypes. Collectively, MORPH2DIAG establishes a fully automated, atlas-free framework that unites unsupervised structural subtype discovery with interpretable machine and deep learning, providing a reproducible foundation for MRI-based morphometric profiling and automated detection of neurodegenerative-like patterns.

PurposeStreamlined-qBOLD (sqBOLD) is a refinement of the quantitative BOLD (qBOLD) technique capable of producing non-invasive and quantitative maps of oxygen extraction fraction (OEF) in a clinically feasible scan time. However, sqBOLD measurements of OEF have been reported as being systematically lower than expected in healthy brain. Since the qBOLD framework infers OEF from the ratio of the reversible transverse relaxation rate (R2') and deoxygenated blood volume (DBV), this underestimation has been attributed the overestimation of DBV. Therefore, this study proposes the use of an independent measure of DBV using hyperoxia-BOLD and investigates whether this results in improved estimates of OEF. MethodsMonte Carlo simulations were used to simulate the qBOLD and hyperoxia-BOLD signals and to compare the systematic and noise related errors of sqBOLD and the new hyperoxia-qBOLD (hqBOLD) technique. Experimentally, sqBOLD and hqBOLD measurements were performed and compared with TRUST (T2 relaxation under spin tagging) based oximetry in the sagittal sinus. ResultsSimulations showed a large improvement in the uncertainty of DBV measurements leading to a much improved dynamic range for OEF measurements with hqBOLD. In a group of ten healthy volunteers, hqBOLD produced measurements of OEF in cortical grey matter (OEFhqBOLD = 38.1 {+/-} 10.1 %) that were not significantly different to TRUST oximetry measures (OEFTRUST = 40.4 {+/-} 7.7 %), whilst sqBOLD derived measures (OEFsqBOLD = 16.1 {+/-} 3.1 %) were found to be significantly different. ConclusionThe simulations and experiments in this study demonstrate that an independent measure of DBV provides improved estimates of OEF.

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations. Markers and expert reviewers manually segmented anatomical MRI data from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Anatomical images showed dramatic differences in myelination and intensities across the 1 to 9 month age range, emphasizing the need for densely sampled gold standard manual segmentations in these ages. The BOBs repository is publicly available through the Masonic Institute for the Developing Brain (MIDB) Open Data Initiative, which links S3 storage, Datalad for version control, and BrainBox for visualization. This repository represents an open-source paradigm, where new additions and changes can be added, enabling a community-driven resource that will improve over time and extend into new ages and protocols. These manual segmentations and the ongoing repository provide a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this repository provides a vitally needed foundation for early-life large-scale studies such as HBCD.

Accurate brain extraction is a critical step in the analysis of rodent head magnetic resonance imaging (MRI) data. However, current methods often encounter difficulties in handling the diverse range of imaging setups, resolutions, and experimental conditions that are commonly found in this field. Based on the Segment Anything Model (SAM), we introduce here SAMson (SAM for Segmentation Of Neuroimages), an automated tool for robust rodent brain extraction. SAMson integrates a bounding box generator and a mask prediction pipeline, offering fully automated and semi-automated modes to address varying experimental complexities. The performance of SAMson was evaluated using three multi-centre rodent MRI datasets annotated at the pixel level, which differed in terms of acquisition parameters, resolution, and animal age groups. SAMson demonstrates superior performance to existing methods, including BET, RBM, and BEN, in terms of segmentation accuracy, with Jaccard indices exceeding 90% across datasets. The semi-automated mode demonstrates particular efficacy in challenging scenarios, including low-resolution images and cases requiring refined mask precision. In contrast to conventional volumetric techniques, SAMson identifies errors at the level of individual slices, thereby enabling rapid and targeted correction when needed. By providing open-source access, SAMson aims to support large-scale research workflows and advance translational neuroscience. The curated data can be downloaded from https://doi.org/10.20350/digitalCSIC/17000, and the code is available at https://github.com/CanalsLab/SAMson.

Functional MRI (fMRI) studies discard the initial volumes acquired during the approach of the magnetization to its steady-state value. Here, we leverage the higher temporal signal-to-noise ratio (tSNR) of these initial volumes to increase the sensitivity of event-related fMRI experiments. To do this, we introduce Acquisition Free Periods (AFPs) that allow for the full recovery of the magnetization, followed by task or baseline acquisition blocks (AB) of fMRI volumes. An appropriately placed stimulus in the AFP produces a Blood Oxygenation-Level-Dependent (BOLD) response that peaks during the initial high tSNR phase of the AB, yielding up to a [~]50% reduction in the number of trials needed to achieve a given statistical threshold relative to conventional fMRI. The silent AFP can be exploited for the presentation of auditory stimuli or uncontaminated electrophysiological recording and its variable duration allows aperiodic stimulus or response-locked signal averaging as well as gating to physiology or motion.

Connectomics--the study of brain networks--provides a unique and valuable opportunity to study the brain. Research in human connectomics, leveraging functional and diffusion Magnetic Resonance Imaging (MRI), is a resource-intensive practice. Typical analysis routines require significant computational capabilities and subject matter expertise. Establishing a pipeline that is low-resource, easy to use, and off-the-shelf (can be applied across multifarious datasets without parameter tuning to reliably estimate plausible connectomes), would significantly lower the barrier to entry into connectomics, thereby democratizing the field by empowering a more diverse and inclusive community of connectomists. We therefore introduce MRI to Graphs (m2g). To illustrate its properties, we used m2g to process MRI data from 35 different studies ({approx} 6,000 scans) from 15 sites without any manual intervention or parameter tuning. Every single scan yielded an estimated connectome that adhered to established properties, such as stronger ipsilateral than contralateral connections in structural connectomes, and stronger homotopic than heterotopic correlations in functional connectomes. Moreover, the connectomes estimated by m2g are more similar within individuals than between them, suggesting that m2g preserves biological variability. m2g is portable, and can run on a single CPU with 16 GB of RAM in less than a couple hours, or be deployed on the cloud using its docker container. All code is available on https://github.com/neurodata/m2g and documentation is available on docs.neurodata.io/m2g.

Thousands of functional magnetic resonance imaging (fMRI) studies have provided important insight into the human brain. However, only a handful of these studies tested infants while they were awake, because of the significant and unique methodological challenges involved. We report our efforts over the past five years to address these challenges, with the goal of creating methods for infant fMRI that can reveal the inner workings of the developing, preverbal mind. We use these methods to collect and analyze two fMRI datasets obtained from infants during cognitive tasks, released publicly with this paper. In these datasets, we explore data quantity and quality, task-evoked activity, and preprocessing decisions to derive and evaluate recommendations for infant fMRI. We disseminate these methods by sharing two software packages that integrate infant-friendly cognitive tasks and behavioral monitoring with fMRI acquisition and analysis. These resources make fMRI a feasible and accessible technique for cognitive neuroscience in human infants.

Heat-related illnesses pose a significant public health challenge in Europe, resulting in increased mortality. Although cold water immersion (CWI) is the most effective treatment for heat stroke, its clinical use is limited. A better understanding of temperature changes in the peripheral body regions can lead to more effective CWI application. Nevertheless, most muscle temperature measurement techniques are invasive. This study evaluated magnetic resonance spectroscopy (MRS) for non-invasive assessment of intramuscular temperature during cold stress and rewarming. Nine healthy volunteers (7 men, 2 women) participated in three 3T MRI sessions: baseline (PRE), immediately after 15 minutes of CWI at 10 degrees to the iliac crest (POST-CWI), and following 100-Watt cycling (POST-cycling). Each scan session included T1w and localized spectroscopy acquisitions in the right thigh. Absolute temperature was estimated from the proton resonance frequency shift between water and creatine peaks. The measurements were split into three groups of voxels, defined as follows: close to the top (TL), bottom (BL), or central (DL) thigh positions. Measurement depth showed a location main effect (p<0.001, p^2=0.40), with DL (35.4[5.9] mm) significantly deeper than TL (22.5[4.2] mm) and BL (25.3[5.1] mm), remaining constant across phases. Temperature decreased significantly from PRE to POST-CWI across all locations (TL: p<0.001, d=2.74; BL: p<0.001, d=1.84; DL: p<0.005, d=1.14). Post-cycling temperature increased at all sites compared to POST-CWI (DL: p=0.040, d=1.06; TL: p<0.001, d=1.7; BL: p<0.001, d=1.80), though TL remained lower than PRE (p<0.017, d=1.48). During POST-CWI, DL showed a significantly higher temperature than TL (p<0.001, d=2.13) and BL (p<0.001, d=2.06). These findings demonstrate that MRS-based temperature mapping provides unique anatomical and thermal characterization of muscle during thermoregulatory stress. While results are promising for understanding CWI mechanisms, validation in larger cohorts is necessary to establish clinical reliability and reproducibility for heat illness management.

Quantitative analysis of positron emission tomography (PET) neuroimaging data is essential for studying neurodegenerative diseases, yet existing processing pipelines often rely on computationally intensive software packages such as FreeSurfer, limiting accessibility for many research groups. Here I introduce BrainPET Studio, an open-source desktop application for atlas-based regional PET quantification that operates entirely in Montreal Neurological Institute (MNI) standard space. BrainPET Studio integrates affine registration, optional Muller-Gartner (MG) partial volume correction (PVC), interactive quality control (QC), and standardized uptake value ratio (SUVR) calculation into a single graphical user interface (GUI), eliminating the requirement for FreeSurfer-based cortical reconstruction. I validated BrainPET Studio against two established pipelines: (1) the UC Berkeley Alzheimers Disease Neuroimaging Initiative (ADNI) AV1451 (flortaucipir) pipeline, which employs FreeSurfer v7.1.1 parcellation, SPM-based coregistration, and Geometric Transfer Matrix (GTM) PVC in native subject space; and (2) the volBrain/petBrain online platform. Region-of-interest (ROI) SUVR values were compared across 322 subjects. Overall Pearson correlation coefficients for meta-ROI composites ranged from r = 0.83-0.96 versus ADNI and r = 0.86-0.94 versus volBrain/petBrain. Detailed per-subject validation on four representative cases across 112 FreeSurfer-defined regions demonstrated strong agreement for large cortical composites and acceptable variability for smaller medial temporal structures. These results establish BrainPET Studio as a reliable, accessible, and extensible tool for multi-site PET research, educational applications, and studies where FreeSurfer-based processing is impractical.